1. Field of the Invention
The present invention relates to a pattern recognizing method and a pattern recognizing apparatus in which an image or a speech is recognized, a pattern identity judging method and a pattern identity judging apparatus in which it is judged according to the pattern recognizing method whether or not an image or a speech is identical with another image or another speech, a recording medium for recording a software program of the pattern recognition and a recording medium for recording a software program of the pattern identity judging method.
2. Description of the Related Art
In a technical field of a pattern recognition such as a face image recognition or a speech recognition, second order statistics (or covariances) of model patterns are calculated from a set of model patterns registered in a data base in advance, a pattern data space is made from the second order statistics, a distribution of an input pattern in the pattern data space (that is, the portion occupied by an input pattern in the pattern data space) is assumed, and features of the input pattern are extracted to recognize the input pattern.
2.1. Previously Proposed Art:
For example, features of the input pattern are extracted according to a well-known Karhunen-Loeve (KL) expansion method. This feature extraction is, for example, disclosed in a literature xe2x80x9cM. Turk, A. Pentland, xe2x80x9cEigenfaces for Recognitionxe2x80x9d, Journal of Congnitive Neuroscience Volume 3, Number 1, 1991xe2x80x9d. Though there are various other methods than the KL expansion method, the other methods are based on the KL expansion method.
In the KL expansion method, each of two patterns Pa and Pb is approximated by a linear combination of basis vectors (the number of vectors is N) Ei (i=1, 2, - - - , N) to produce an approximated pattern, and the collation between the patterns Pa and Pb is performed by using approximated patterns A and B. The approximated patterns A and B are formulated as follows.                               A          =                                    ∑                              i                =                1                            N                        ⁢                          xe2x80x83                        ⁢                          αi              ⁢                              xe2x80x83                            ⁢              Ei                                      ⁢                  
                ⁢                  B          =                                    ∑                              i                =                1                            N                        ⁢                          βi              ⁢                              xe2x80x83                            ⁢              Ei                                                          (        1        )            
In the KL expansion method, a covariance matrix is obtained from W pieces of teaching pattern data, an eigenvalue is calculated for each eigenvector of the covariance matrix, N eigenvectors corresponding to N higher eigenvalues (the number N is, for example, 100) are selected as N basis vectors Ei from all eigenvectors of the covariance matrix.
In cases where a pattern data space is defined by the N basis vectors, there are two merits.
(1) The W teaching pattern data projected on each plane defined by two basis vectors are separated from each other to a highest degree. Therefore, the W teaching pattern data can be easily distinguished from each other.
(2) Noises included in the patterns Pa and Pb and changes occurring randomly in the patterns Pa and Pb can be removed.
In the KL expansion method, it is supposed that an assuming precision for distribution parameters calculated from a pattern model set is sufficiently high. For example, in a face image recognition, in cases where a statistic property in a process for obtaining a pattern set agrees with that in a process for obtaining another pattern set, many examinations indicate that a pattern recognition can be performed at a very high precision rate and the collation of the pattern sets can be correctly performed.
2.2. Problems to be Solved by the Invention:
However, in cases where features of a model pattern are extracted according to two types of image receiving processes, there is a case that a first set of teaching pattern data obtained from the model pattern according to the first process greatly differs from a second set of teaching pattern data obtained from the same model pattern according to the second process, so that a statistic property for the first set of teaching pattern data greatly differs from that for the second set of teaching pattern data. For example, in cases where a lighting condition for photographing a first pattern differs from that for photographing a second pattern, there is a case that a statistic property for a first set of pattern data obtained from the first pattern differs from that for a second set of pattern data obtained from the second pattern. As a result, even though features of the first pattern agree with those of the second pattern, because an image recognition for the first and second sets of pattern data is not performed with sufficiently high precision, the collation of the first and second sets of pattern data with each other is not correctly performed, and the identity of the first pattern with the second pattern cannot be judged.
The above problem is based on the supposition that two pattern data sets compared with each other are derived from the common distribution (or the common statistic parameters). Therefore, in cases where two pattern data sets compared with each other are derived from different distributions (or different statistic parameters), the KL expansion method cannot be properly performed in the pattern recognition or the pattern collation.
A first object of the present invention is to provide, with due consideration to the drawbacks of such conventional pattern recognizing method and apparatus, pattern recognizing method and apparatus in which an input pattern identical with one of referential patterns is recognized with high precision even though a process for obtaining the input pattern of an input sample differs from a process for obtaining the referential patterns from referential samples.
A second object of the present invention is to provide pattern identity judging method and apparatus in which the identity of a first input pattern obtained according to a first process with a second input pattern obtained according to a second process is correctly judged regardless of a statistic property difference between the first and second input patterns occurred according to a difference between the first and second processes.
A third object of the present invention is to provide a recording medium in which a software program of the pattern recognizing method or a software program of the pattern identity judging method is recorded.
The first object is achieved by the provision of a pattern recognizing method, comprising the steps of:
obtaining a set of first teaching patterns of a plurality of teaching samples according to a first pattern obtaining process;
obtaining a set of second teaching patterns of the teaching samples according to a second pattern obtaining process differing from the first pattern obtaining process;
calculating a teaching pattern distribution from the set of first teaching patterns or the set of second teaching patterns;
calculating a teaching distribution of a perturbation between the set of first teaching patterns and the set of second teaching patterns;
calculating a feature extraction matrix, which minimizes an overlapping area between the teaching pattern distribution and the teaching perturbation distribution, from the teaching pattern distribution and the teaching perturbation distribution;
obtaining a set of referential patterns of a plurality of referential samples according to the first pattern obtaining process;
calculating a set of referential feature patterns of the referential samples from the set of referential patterns according to the feature extraction matrix, the set of referential feature patterns being independent of the first pattern obtaining process and the second pattern obtaining process;
receiving an input pattern of an input sample according to the second pattern obtaining process;
calculating an input feature pattern of the input sample from the input pattern according to the feature extraction matrix;
selecting a specific referential feature pattern most similar to the input feature pattern from the set of referential feature patterns; and
recognizing a specific referential sample corresponding to the specific referential feature pattern as the input sample.
The first object is also achieved by the provision of a pattern recognizing apparatus, comprising:
first pattern obtaining means for obtaining a set of first teaching patterns of a plurality of teaching samples according to a first pattern obtaining process;
second pattern obtaining means for obtaining a set of second teaching patterns of the teaching samples according to a second pattern obtaining process differing from the first pattern obtaining process;
feature extracting means for calculating a teaching pattern distribution from the set of first teaching patterns obtained by the first pattern obtaining means or the set of second teaching patterns obtained by the second pattern obtaining means, calculating a teaching distribution of a perturbation between the set of first teaching patterns and the set of second teaching patterns, and calculating a feature extraction matrix, which minimizes an overlapping area between the teaching pattern distribution and the teaching perturbation distribution, from the teaching pattern distribution and the teaching perturbation distribution;
referential feature pattern calculating means for obtaining a set of referential patterns of a plurality of referential samples according to the first pattern obtaining process, and calculating a set of referential feature patterns of the referential samples from the set of referential patterns according to the feature extraction matrix calculated by the feature extracting means to make the set of referential feature patterns independent of the first pattern obtaining process and the second pattern obtaining process; and
input pattern recognizing means for receiving an input pattern of an input sample according to the second pattern obtaining process, calculating an input feature pattern of the input sample from the input pattern according to the feature extraction matrix calculated by the feature extracting means, selecting a specific referential feature pattern most similar to the input feature pattern from the set of referential feature patterns calculated by the referential feature pattern calculating means, and recognizing a specific referential sample corresponding to the specific referential feature pattern as the input sample.
In the above steps and configuration, in cases where a pattern obtaining process adopted to obtain a set of first patterns differs from a pattern obtaining process adopted to obtain a set of second patterns, a statistic property difference between the set of first patterns and the set of second patterns is generated. Therefore, even though one first pattern and one second pattern are obtained from the same sample, it is difficult to judge that the first pattern is identical with the second pattern.
In the present invention, a feature extraction matrix, which minimizes an overlapping area between the teaching pattern distribution and the teaching perturbation distribution, is calculated. Therefore, in cases where a feature extraction transformation using the feature extraction matrix is performed for the set of referential patterns to calculate the set of referential feature patterns, a referential pattern distribution and a referential perturbation distribution in the set of referential feature patterns has the same group of distribution axes and become orthogonal to each other, so that perturbation components coming in the set of referential patterns can be removed in the set of referential feature patterns. This removal of the perturbation components from the set of referential patterns denotes that the set of referential feature patterns becomes independent of the first pattern obtaining process and the second pattern obtaining process. Also, the input feature pattern becomes independent of the first pattern obtaining process and the second pattern obtaining process.
Accordingly, a specific referential sample corresponding to a specific referential feature pattern most similar to the input feature pattern can be correctly recognized as the input sample regardless of the first pattern obtaining process and the second pattern obtaining process.
It is preferred that the step of calculating a teaching pattern distribution comprise the step of
assuming a teaching pattern covariance matrix of a pattern sample space as the teaching pattern distribution,
the step of calculating a teaching distribution of a perturbation comprise the steps of
calculating a teaching pattern perturbation between one first teaching pattern of one teaching sample and one second teaching pattern of the teaching sample for each teaching sample; and
assuming a teaching perturbation covariance matrix from the teaching pattern perturbations as the teaching perturbation distribution, and
the step of calculating a feature extraction matrix comprise the steps of
calculating a both-diagonalizing matrix, which diagonalizes both the teaching pattern covariance matrix and the teaching perturbation covariance matrix, from the teaching pattern covariance matrix and the teaching perturbation covariance matrix;
diagonalizing the teaching pattern covariance matrix according to the both-diagonalizing matrix to produce a diagonal matrix of the teaching pattern covariance matrix;
diagonalizing the teaching perturbation covariance matrix according to the both-diagonalizing matrix to produce a diagonal matrix of the teaching perturbation covariance matrix;
calculating an amplitude re-transformation matrix, which again transforms a referential pattern covariance matrix indicated by the set of referential feature patterns to adjust amplitudes of diagonal elements of the referential pattern covariance matrix after the referential pattern covariance matrix is transformed by the both-diagonalizing matrix to be diagonalized, from the diagonal matrices; and
calculating the feature extraction matrix from the both-diagonalizing matrix and the amplitude re-transformation matrix.
Also, it is preferred that the feature extracting means comprise
pattern covariance assuming means for calculating a teaching pattern covariance matrix of a pattern sample space from the first teaching patterns or the second teaching patterns and assuming the teaching pattern covariance matrix as the teaching pattern distribution;
pattern perturbation calculating means for calculating a teaching pattern perturbation between one first teaching pattern of one teaching sample and one second teaching pattern of the teaching sample for each teaching sample;
perturbation covariance assuming means for assuming a teaching perturbation covariance matrix from the teaching pattern perturbations calculated by the pattern perturbation calculating means as the teaching perturbation distribution;
both-diagonalizing matrix calculating means for calculating a both-diagonalizing matrix, which diagonalizes both the teaching pattern covariance matrix assumed by the pattern covariance assuming means and the teaching perturbation covariance matrix assumed by the perturbation covariance assuming means, from the teaching pattern covariance matrix and the teaching perturbation covariance matrix;
diagonal matrix producing means for diagonalizing the teaching pattern covariance matrix assumed by the pattern covariance assuming means according to the both-diagonalizing matrix calculated by the both-diagonalizing matrix calculating means to produce a diagonal matrix of the teaching pattern covariance matrix, and diagonalizing the teaching perturbation covariance matrix assumed by the perturbation covariance assuming means according to the both-diagonalizing matrix to produce a diagonal matrix of the teaching perturbation covariance matrix;
amplitude re-transformation matrix calculating means for calculating an amplitude re-transformation matrix, which again transforms a referential pattern covariance matrix indicated by the set of referential feature patterns to be calculated by the referential feature pattern calculating means to adjust amplitudes of diagonal elements of the referential pattern covariance matrix after the referential pattern covariance matrix is transformed by the both-diagonalizing matrix calculated by the both-diagonalizing matrix calculating means to be diagonalized, from the diagonal matrices; and
calculating the feature extraction matrix from the both-diagonalizing matrix calculated by the both-diagonalizing matrix calculating means and the amplitude re-transformation matrix calculated by the amplitude re-transformation matrix calculating means.
In the above steps and configuration, the teaching pattern covariance matrix derived from the set of first teaching patterns and the teaching perturbation covariance matrix derived from the set of first teaching patterns and the set of second teaching patterns have the same group of eigenvectors by diagonalizing the teaching pattern covariance matrix and the teaching perturbation covariance matrix by using the both-diagonalizing matrix. In addition, the diagonal elements of the teaching pattern covariance matrix and the diagonal elements of the teaching perturbation covariance matrix are adjusted by using the amplitude re-transformation matrix, so that not only the teaching pattern covariance matrix and the teaching perturbation covariance matrix have the same group of eigenvectors, but also the order of the eigenvectors arranged in the order of decreasing eigenvalues (or variance values) in the teaching pattern covariance matrix can be set to the reverse of the order of the eigenvectors arranged in the order of decreasing eigenvalues (or variance values) in the teaching perturbation covariance matrix.
Therefore, in cases where a feature extraction transformation using the feature extraction matrix is performed for the teaching pattern covariance matrix indicating a pattern distribution of the first teaching patterns and the teaching perturbation covariance matrix indicating a perturbation distribution between the set of first teaching patterns and the set of second teaching patterns, a pattern sample space occupied by the pattern distribution has the same group of distribution axes (or the same group of basic vectors) as those of a pattern sample space occupied by the perturbation distribution, and the order of the spreading degrees of the pattern distribution in directions of the axes is the reverse of the order of the spreading degrees of the perturbation distribution in directions of the axes. This reverse relationship in the spreading degrees between the pattern distribution and the perturbation distribution indicates a condition that the pattern distribution is orthogonal to the perturbation distribution, and an overlapping area between the pattern distribution and the perturbation distribution is minimized. The minimization of the overlapping area indicates that perturbation components coming in the pattern sample space of the first teaching patterns are effectively removed. Because the perturbation components denote a statistic property difference between the first teaching patterns and the second teaching patterns, a pattern recognition independent of a statistic property difference between the first obtaining process and the second obtaining process can be performed in cases where a feature extraction transformation using the feature extraction matrix is performed for the first teaching patterns.
Also, it is preferred that the step of calculating a teaching distribution of a perturbation comprise the step of
calculating a teaching perturbation distribution between one first teaching pattern of one teaching sample and one second teaching pattern of the teaching sample for each teaching sample,
the step of calculating a feature extraction matrix comprise the step of
calculating a feature extraction matrix, which minimizes an overlapping area between a teaching pattern distribution of one teaching sample and the teaching perturbation distribution of the teaching sample, from the teaching pattern distribution and the teaching perturbation distribution of the teaching sample for each teaching sample,
the step of obtaining a set of referential patterns comprise the step of
obtaining a set of referential patterns of the teaching samples according to the first pattern obtaining process or the second pattern obtaining process,
the step of calculating a set of referential feature patterns comprise the step of
calculating one referential feature pattern of one teaching sample from one referential pattern of the teaching sample according to the feature extraction matrix of the teaching sample for each teaching sample,
the step of calculating an input feature pattern comprise the step of
calculating an input feature pattern of the input sample from the input pattern according to the feature extraction matrix of one teaching sample for each teaching sample, and
the step of selecting a specific referential feature pattern comprise the steps of
estimating a similarity between one input feature pattern corresponding to one teaching sample and one referential feature pattern of the same teaching sample; and
selecting a specific referential feature pattern of a specific teaching sample most similar to the input feature pattern corresponding to the teaching sample from the set of referential feature patterns.
Also, the first object is achieved by the provision of a pattern recognizing apparatus, comprising:
first pattern obtaining means for obtaining a set of first teaching patterns of a plurality of registered samples according to a first pattern obtaining process;
second pattern obtaining means for obtaining a set of second teaching patterns of the registered samples according to a second pattern obtaining process differing from the first pattern obtaining process;
feature extracting means for calculating a teaching pattern distribution from the first teaching patterns obtained by the first pattern obtaining means or the second teaching patterns obtained by the second pattern obtaining means, calculating a teaching distribution of a perturbation between one first teaching pattern of one registered sample and one second teaching pattern of the registered sample for each registered sample, and calculating a feature extraction matrix, which minimizes an overlapping area between the teaching pattern distribution of one registered sample and the teaching perturbation distribution of the registered sample, from the teaching pattern distribution and the teaching perturbation distribution for each registered sample;
referential feature pattern calculating means for obtaining a set of referential patterns of the registered samples according to the first pattern obtaining process, and calculating a referential feature pattern of one registered sample from one referential pattern of the registered sample according to the feature extraction matrix of the registered sample calculated by the feature extracting means for each registered sample to make each referential feature pattern independent of the first pattern obtaining process and the second pattern obtaining process; and
input pattern recognizing means for receiving an input pattern of an input sample according to the second pattern obtaining process, calculating an input feature pattern corresponding to one registered sample from the input pattern according to the feature extraction matrix of the registered sample calculated by the feature extracting means for each registered sample, estimating a similarity between one referential feature pattern of one registered sample and the input feature pattern corresponding to the registered sample for each registered sample, selecting a specific referential feature pattern most similar to the input feature pattern from the referential feature patterns calculated by the referential feature pattern calculating means, and recognizing a specific registered sample corresponding to the specific referential feature pattern as the input sample.
In the above steps and configuration, a teaching perturbation distribution between one first teaching pattern of one teaching sample and one second teaching pattern of the teaching sample is calculated for each teaching sample, a feature extraction matrix is calculated for each teaching sample, and one referential feature pattern corresponding to one teaching sample is calculated from one referential pattern of the registered sample according to the first pattern obtaining process by using the feature extraction matrix for each teaching sample. Therefore, each referential feature pattern becomes independent of the first pattern obtaining process and the second pattern obtaining process.
When an input pattern of an input sample is received according to the second pattern obtaining process, because an input feature pattern is calculated from the input pattern according to the feature extraction matrix for each registered sample, each input feature pattern becomes independent of the first pattern obtaining process and the second pattern obtaining process.
Accordingly, even though the pattern obtaining process for obtaining the input pattern differs from that for obtaining the referential patterns, in cases where a similarity between one referential feature pattern and one input feature pattern is estimated for each registered sample, a specific referential feature pattern most similar to the input feature pattern can be selected from the referential feature patterns, and a specific registered sample corresponding to the specific referential feature pattern can be recognized as the input sample.
The second object is achieved by the provision of a pattern identity judging method, comprising the steps of:
obtaining a set of first teaching patterns of a plurality of teaching samples according to a first pattern obtaining process;
obtaining a set of second teaching patterns of the teaching samples according to a second pattern obtaining process differing from the first pattern obtaining process;
calculating a teaching pattern distribution from the set of first teaching patterns or the set of second teaching patterns;
calculating a teaching distribution of a perturbation between the set of first teaching patterns and the set of second teaching patterns;
calculating a feature extraction matrix, which minimizes an overlapping area between the teaching pattern distribution and the teaching perturbation distribution, from the teaching pattern distribution and the teaching perturbation distribution;
receiving a first input pattern of a first input sample according to the first pattern obtaining process;
calculating a first input feature pattern of the first input sample from the first input pattern according to the feature extraction matrix, the first input feature pattern being independent of the first pattern obtaining process and the second pattern obtaining process;
receiving a second input pattern of a second input sample according to the second pattern obtaining process;
calculating a second input feature pattern of the second input sample from the second input pattern according to the feature extraction matrix, the second input feature pattern being independent of the first pattern obtaining process and the second pattern obtaining process;
collating the first input feature pattern with the second input feature pattern to estimate a similarity between the first input sample and the second input sample; and
judging that the first input sample is identical with the second input sample in cases where the similarity is high.
The second object is also achieved by the provision of a pattern identity judging apparatus, comprising:
first pattern obtaining means for obtaining a set of first teaching patterns of a plurality of teaching samples according to a first pattern obtaining process;
second pattern obtaining means for obtaining a set of second teaching patterns of the teaching samples according to a second pattern obtaining process differing from the first pattern obtaining process;
feature extracting means for calculating a teaching pattern distribution from the set of first teaching patterns obtained by the first pattern obtaining means or the set of second teaching patterns obtained by the second pattern obtaining means, calculating a teaching distribution of a perturbation between the set of first teaching patterns and the set of second teaching patterns, and calculating a feature extraction matrix, which minimizes an overlapping area between the teaching pattern distribution and the teaching perturbation distribution, from the teaching pattern distribution and the teaching perturbation distribution;
feature pattern calculating means for receiving a first input pattern of a first input sample according to the first pattern obtaining process, receiving a second input pattern of a second input sample according to the second pattern obtaining process, calculating a first input feature pattern of the first input sample from the first input pattern according to the feature extraction matrix calculated by the feature extracting means to make the first input feature pattern independent of the first pattern obtaining process and the second pattern obtaining process, and calculating a second input feature pattern of the second input sample from the second input pattern according to the feature extraction matrix to make the second input feature pattern independent of the first pattern obtaining process and the second pattern obtaining process; and
identity judging means for collating the first input feature pattern calculated by the feature pattern calculating means with the second input feature pattern calculated by the feature pattern calculating means to estimate a similarity between the first input sample and the second input sample, and judging that the first input sample is identical with the second input sample in cases where the similarity is high.
In the above steps and configuration, the feature extraction matrix is calculated in the same manner as in the pattern recognizing method. Therefore, the first input feature pattern and the second input feature pattern calculated by using the feature extraction matrix are independent of the first pattern obtaining process and the second pattern obtaining process. In this case, because the first input feature pattern derived from the first pattern obtaining process can correctly collate with the second input feature pattern derived from the second pattern obtaining process, in cases where the first input sample is actually identical with the second input sample, the judgement that the first input sample is identical with the second input sample can be reliably performed.
The second object is also achieved by the provision of a pattern identity judging apparatus, comprising:
first pattern obtaining means for obtaining a set of first teaching patterns of a plurality of teaching samples according to a first pattern obtaining process;
second pattern obtaining means for obtaining a group of second teaching patterns according to a second pattern obtaining process differing from the first pattern obtaining process for each teaching sample;
feature extracting means for calculating a teaching pattern distribution from the set of first teaching patterns obtained by the first pattern obtaining means or the groups of second teaching patterns obtained by the second pattern obtaining means, calculating a teaching distribution of a perturbation between one first teaching pattern of one teaching sample and the group of second teaching patterns of the teaching sample for each teaching sample, calculating an average teaching perturbation distribution from the teaching perturbation distributions, and calculating a feature extraction matrix, which minimizes an overlapping area between the teaching pattern distribution and the average teaching perturbation distribution, from the teaching pattern distribution and the average teaching perturbation distribution;
feature pattern calculating means for receiving a first input pattern of a first input sample according to the first pattern obtaining process, receiving a second input pattern of a second input sample according to the second pattern obtaining process, calculating a first input feature pattern of the first input sample from the first input pattern according to the feature extraction matrix calculated by the feature extracting means to make the first input feature pattern independent of the first pattern obtaining process and the second pattern obtaining process, and calculating a second input feature pattern of the second input sample from the second input pattern according to the feature extraction matrix to make the second input feature pattern independent of the first pattern obtaining process and the second pattern obtaining process; and
identity judging means for collating the first input feature pattern calculated by the feature pattern calculating means with the second input feature pattern calculated by the feature pattern calculating means to estimate a similarity between the first input sample and the second input sample, and judging that the first input sample is identical with the second input sample in cases where the similarity is high.
In the above configuration, a group of second teaching patterns are obtained according to a second pattern obtaining process for each teaching sample, a teaching perturbation distribution between one first teaching pattern of one teaching sample and the group of second teaching patterns of the teaching sample is calculated for each teaching sample, an average teaching perturbation distribution is calculated from the teaching perturbation distributions, and a feature extraction matrix is calculated from the teaching pattern distribution and the average teaching perturbation distribution.
Therefore, even though the pattern obtaining process for obtaining a first input pattern of a first input sample differs from that for obtaining a second input pattern of a second input sample, because a first input feature pattern of the first input sample and a second input feature pattern of the second input sample are calculated according to the feature extraction matrix, the first input feature pattern and the second input feature pattern become independent of the first pattern obtaining process and the second pattern obtaining process.
Accordingly, in cases where the first input feature pattern is collated with the second input feature pattern to estimate a similarity between the first input sample and the second input sample, and the judgement whether or not the first input sample is identical with the second input sample can be performed according to the similarity.
The third object is achieved by the provision of a recording medium for recording a software program of a pattern recognizing method executed in a computer, the pattern recognizing method, comprising the steps of:
obtaining a set of first teaching patterns of a plurality of teaching samples according to a first pattern obtaining process;
obtaining a set of second teaching patterns of the teaching samples according to a second pattern obtaining process differing from the first pattern obtaining process;
calculating a teaching pattern distribution from the set of first teaching patterns or the set of second teaching patterns;
calculating a teaching distribution of a perturbation between the set of first teaching patterns and the set of second teaching patterns;
calculating a feature extraction matrix, which minimizes an overlapping area between the teaching pattern distribution and the teaching perturbation distribution, from the teaching pattern distribution and the teaching perturbation distribution;
obtaining a set of referential patterns of a plurality of referential samples according to the first pattern obtaining process or the second pattern obtaining process;
calculating a set of referential feature patterns of the referential samples from the set of referential patterns according to the feature extraction matrix, the set of referential feature patterns being independent of the first pattern obtaining process and the second pattern obtaining process;
receiving an input pattern of an input sample according to the first pattern obtaining process or the second pattern obtaining process;
calculating an input feature pattern of the input sample from the input pattern according to the feature extraction matrix;
selecting a specific referential feature pattern most similar to the input feature pattern from the set of referential feature patterns; and
recognizing a specific referential sample corresponding to the specific referential feature pattern as the input sample.
In the above recording medium, a software program of the pattern recognizing method can be recorded. Therefore, the software program of the pattern recognizing method can be executed in a computer.
The third object is also achieved by the provision of a recording medium for recording a software program of a pattern identity judging method executed in a computer, the pattern identity judging method, comprising the steps of:
obtaining a set of first teaching patterns from a plurality of teaching samples according to a first pattern obtaining process;
obtaining a set of second teaching patterns from the teaching samples according to a second pattern obtaining process differing from the first pattern obtaining process;
calculating a teaching pattern distribution from the set of first teaching patterns or the set of second teaching patterns;
calculating a teaching distribution of a perturbation between the set of first teaching patterns and the set of second teaching patterns;
calculating a feature extraction matrix, which minimizes an overlapping area between the teaching pattern distribution and the teaching perturbation distribution, from the teaching pattern distribution and the teaching perturbation distribution;
receiving a first input pattern of a first input sample according to the first pattern obtaining process;
calculating a first input feature pattern of the first input sample from the first input pattern according to the feature extraction matrix, the first input feature pattern being independent of the first pattern obtaining process and the second pattern obtaining process;
receiving a second input pattern of a second input sample according to the second pattern obtaining process;
calculating a second input feature pattern of the second input sample from the second input pattern according to the feature extraction matrix, the second input feature pattern being independent of the first pattern obtaining process and the second pattern obtaining process;
collating the first input feature pattern with the second input feature pattern to estimate a similarity between the first input sample and the second input sample; and
judging that the first input sample is identical with the second input sample in cases where the similarity is high.
In the above recording medium, a software program of the pattern identity judging method can be recorded. Therefore, the software program of the pattern identity judging method can be executed in a computer.